Abstract
Mental health has become a major concern due to changing lifestyles and ever-increasing pressure at the workplace. Deadlines and goals are the prime reason for stress, which in turn leads to depression, anxiety as well as other mental illnesses. Hence, in the lure to improve the current situation, this paper proposed an ensemble stress detection mechanism that conveniently and accurately detects stress, depression as well anxiety. Few steps are conducted to detect the mental issue of any individual undertaking the test. The proposed ensemble mechanism comprises four basic detection modes: face detection, voice detection, Depression Anxiety Stress Scale (DASS), and a 22-parameter test. Face detection is a reliable source for detecting mental issues, whereas voice recognition confirms and aids the result provided by face detection. In addition, DASS test is a simple questionnaire conducted with a scaled answering system ranging from high to low, and finally, the 22-parameter test consists of 22 important physiological features of the patient. Experimental findings on different machine-learning datasets show that the proposed ensemble approach for stress detection is promising.
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Ahuja, R., Roul, R.K. (2023). An Ensemble Technique to Detect Stress in Young Professional. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_60
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